Can Neural Networks Learn Nanoscale Friction?
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arXiv
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| Format: | Preprint |
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2024
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| author | Shabani, Mahboubeh Silva, Andrea Pellegrini, Franco Wang, Jin Buzio, Renato Gerbi, Andrea Vanossi, Andrea Sadeghi, Ali Tosatti, Erio |
| author_facet | Shabani, Mahboubeh Silva, Andrea Pellegrini, Franco Wang, Jin Buzio, Renato Gerbi, Andrea Vanossi, Andrea Sadeghi, Ali Tosatti, Erio |
| contents | Current nanofriction experiments on crystals, both tip-on-surface and surface-on-surface, provide force traces as their sole output, typically exhibiting atomic size stick-slip oscillations. Physically interpreting these traces is a task left to the researcher. Historically done by hand, it generally consists in identifying the parameters of a Prandtl-Tomlinson (PT) model that best reproduces these traces. This procedure is both work-intensive and quite uncertain. We explore in this work how machine learning (ML) could be harnessed to do that job with optimal results, and minimal human work. A set of synthetic force traces is produced by PT model simulations covering a large span of parameters, and a simple neural network (NN) perceptron is trained with it. Once this trained NN is fed with experimental force traces, it will ideally output the PT parameters that best approximate them. By following this route step by step, we encountered and solved a variety of problems which proved most instructive and revealing. In particular, and very importantly, we met unexpected inaccuracies with which one or another parameter was learned by the NN. The problem, we then show, could be eliminated by proper manipulations and augmentations operated on the training force traces, and that without extra efforts and without injecting experimental informations. Direct application to the sliding of a graphene coated AFM tip on a variety of 2D materials substrates validates and encourages use of this ML method as a ready tool to rationalise and interpret future stick-slip nanofriction data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06677 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can Neural Networks Learn Nanoscale Friction? Shabani, Mahboubeh Silva, Andrea Pellegrini, Franco Wang, Jin Buzio, Renato Gerbi, Andrea Vanossi, Andrea Sadeghi, Ali Tosatti, Erio Mesoscale and Nanoscale Physics Materials Science Chemical Physics Computational Physics Current nanofriction experiments on crystals, both tip-on-surface and surface-on-surface, provide force traces as their sole output, typically exhibiting atomic size stick-slip oscillations. Physically interpreting these traces is a task left to the researcher. Historically done by hand, it generally consists in identifying the parameters of a Prandtl-Tomlinson (PT) model that best reproduces these traces. This procedure is both work-intensive and quite uncertain. We explore in this work how machine learning (ML) could be harnessed to do that job with optimal results, and minimal human work. A set of synthetic force traces is produced by PT model simulations covering a large span of parameters, and a simple neural network (NN) perceptron is trained with it. Once this trained NN is fed with experimental force traces, it will ideally output the PT parameters that best approximate them. By following this route step by step, we encountered and solved a variety of problems which proved most instructive and revealing. In particular, and very importantly, we met unexpected inaccuracies with which one or another parameter was learned by the NN. The problem, we then show, could be eliminated by proper manipulations and augmentations operated on the training force traces, and that without extra efforts and without injecting experimental informations. Direct application to the sliding of a graphene coated AFM tip on a variety of 2D materials substrates validates and encourages use of this ML method as a ready tool to rationalise and interpret future stick-slip nanofriction data. |
| title | Can Neural Networks Learn Nanoscale Friction? |
| topic | Mesoscale and Nanoscale Physics Materials Science Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2412.06677 |